I think it is not terrible if our system make a false alarm. After all, computers always make mistakes, it is feasible as long as their frequency of making mistakes can be controlled to a very low rate. And there is a metric which is widely used in evaluating systems for detecting things. We can measure our system with these values.
Precision,Recall and F-measure
Precision measures the percentage of the items that the system detected(i.e., the system labeled as positive) that are in fact positive.
Recall measures the percentage of items actually present in the input that were correctly identified by the system.
F-measure is a combination of precision and recall.
We should improve the recall if we want to reduce the possibility of false alarm. I have come up with a good idea to solve the problem that means the system will not exclusively rely on a pattern matcher to deal with the false alarm problem.
And I will continue testing my detection module. Once the result come out, I’ll inform you here if you don’t mind. After that, maybe I need to revise my proposal to keep everything fit in to 3 months.